Deploying AI on the Buyside: A 20-Year Engineer's Playbook
In this episode, Brett Caughran and Khe Hy sit down with Matt Stockton, a software engineer with 20 years of experience working with firms to architect data infrastructure and deploy AI capabilities. Matt argues that the AI model itself is rarely the hard part. Instead, the real advantage is unlocked when you know your own process and data well enough to write it down.
We get into:
Why the bottleneck in deploying AI is rarely the AI itself
The shift from "single-player" AI (one person with one laptop) to "multiplayer" (AI across a whole firm)
Building a shared "company resource brain" from knowledge scattered across emails, notes, and drives
Why you systematize one or two workflows instead of boiling the ocean
Structured databases vs. folder-and-markdown setups, and use cases for each approach
Excel case study: how a 1.2-million-token model choked, and how to break the task down instead
Why writing down your process is 80% of the work and the voice-memo trick Matt uses to get it out of his head
The "bitter lesson" of why fixes get obsoleted by the next model, and how to build things that last
Building durable, reusable skills and "hill climbing" them with red-pen feedback
Matching the tool to the job: frontier intelligence for judgment, cheaper models for deterministic tasks
How a real "aha" moment could come from trying the one thing you're sure AI can't do yet
We're not coming at this as "experts" with all the answers. We're in it every day, testing, breaking things, and trying to understand where this is going. The goal of the podcast is simple: bring you along as we learn, and give you a clearer view of how AI is actually being used in investing. If you work in equity research, at a hedge fund, or on the buyside and you're trying to make sense of AI, this is a good place to start.
Chapters (Timestamps)
[00:00] Intro
[00:59] — 20 Years of Software, From Data Infra to LLMs
[02:23] — Single-Player to Multiplayer: The Local-Machine Problem
[03:18] — Building a Company "Resource Brain"
[05:57] — Folder Structures and Markdown vs. Relational Databases
[07:48] — The Excel Problem: 1.2M-Token Financial Models
[10:46] — The Bitter Lesson of AI Engineering
[12:36] — How Time-Crunched CIOs Actually Stay Current
[15:42] — AI Psychosis and the Aha Moment
[17:27] — Turning a Research Doc Into a Shareable Website
[20:06] — Bucketing the Deployment Problem: Job to Be Done
[23:29] — Investors as Intuitive Pianists: The Articulation Problem
[24:07] — The Voice-Memo Hack for Extracting Your Own Process
[25:22] — Why It's Not a Tech Problem
[28:49] — The Last Mile: Getting From Prototype to Production
[29:42] — Assembling Existing Tools vs. Building Custom
[32:06] — Moving Beyond Basic Synthesis Skills
[34:20] — Skill Creation, Hill Climbing, and the Red Pen
[36:27] — Building Evals and LLM-as-Judge
[40:06] — Debugging the Model: The Goodwill Impairment Trap
[42:25] — The Tool Stack: Claude Code, Codex, and Mobile
[49:45] — Chinese Models, Open Weights, and Token Efficiency
[52:13] — Frontier Intelligence for Judgment, Cheap Models for the Rest
Want to actually build these workflows yourself?
The AI Accelerator is Fundamental Edge's 6-month cohort for investors who want repeatable AI workflows. Learn More below:
https://www.fundamentedge.com/ai-accelerator
Watch the full podcast series on our site:https://www.fundamentedge.com/invest-with-ai
Follow Invest with AI on:
Spotify: https://open.spotify.com/show/033xcEEovVViS7hIYwNuGZ
Apple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892